Recently, ultra-widefield (UWF) 200\degree~fundus imaging by Optos cameras has gradually been introduced because of its broader insights for detecting more information on the fundus than regular 30 degree - 60 degree fundus cameras. Compared with UWF fundus images, regular fundus images contain a large amount of high-quality and well-annotated data. Due to the domain gap, models trained by regular fundus images to recognize UWF fundus images perform poorly. Hence, given that annotating medical data is labor intensive and time consuming, in this paper, we explore how to leverage regular fundus images to improve the limited UWF fundus data and annotations for more efficient training. We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training. A consistency regularization term is proposed in the loss of the GAN to improve and regulate the quality of the generated data. Our method does not require that images from the two domains be paired or even that the semantic labels be the same, which provides great convenience for data collection. Furthermore, we show that our method is robust to noise and errors introduced by the generated unlabeled data with the pseudo-labeling technique. We evaluated the effectiveness of our methods on several common fundus diseases and tasks, such as diabetic retinopathy (DR) classification, lesion detection and tessellated fundus segmentation. The experimental results demonstrate that our proposed method simultaneously achieves superior generalizability of the learned representations and performance improvements in multiple tasks.
UWAFA-GAN: Ultra-Wide-Angle Fluorescein Angiography Transformation via Multi-scale Generation and Registration Enhancement
Hangzhou Dianzi University（杭州电子科技大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen University（深圳大学）；Misr Higher Institute for Commerce and Computers（米斯尔高等商务与计算机学院）
Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images
ASRI, Dept. of ECE, Seoul National University（首尔大学电子与计算机工程系ASRI）；Dept. of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University（成均馆大学三星首尔医院眼科）；School of Electrical Engineering, Kookmin University（国民大学电气工程学院）
Airdoc LLC（爱丁堡医疗科技有限公司）；Monash University（莫纳什大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）
For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus imaging by Optos camera is gradually put into use because of its broader insights on fundus for some lesions that are not typically seen in traditional fundus images. Research on traditional fundus images is an active topic but studies on UWF fundus images are few. One of the most important reasons is that UWF fundus images are hard to obtain. In this paper, for the first time, we explore domain adaptation from the …
Automated Artifact Detection in Ultra-widefield Fundus Photography of Patients with Sickle Cell Disease
Johns Hopkins School of Medicine（约翰斯·霍普金斯医学院）；Wilmer Eye Institute（威尔眼科研究所）；Morehouse School of Medicine（莫尔豪斯医学院）；Drexel University College of Medicine（德雷塞尔大学医学院）；Malone Center for Engineering in Healthcare（马龙医疗保健工程中心）
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Southwest University（西南大学）；Taiyuan University（太原学院）；Taiyuan University of Technology（太原理工大学）；Shanxi Eye Hospital（山西省眼科医院）；Nanjing University of Information Science and Technology（南京信息工程大学）
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection. We first improve on the s…
OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
School of Data Science, Fudan University（复旦大学数据科学学院）；The Hong Kong Polytechnic University（香港理工大学）；Eye & ENT Hospital, Fudan University（复旦大学眼耳鼻喉科医院）；College of Business and Economics, Australian National University（澳大利亚国立大学商学与经济学院）
Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images
Department of Applied Biology；Chemical Technology, The Hong Kong Polytechnic University；Department of Data Science \& AI, The Hong Kong Polytechnic University e2；School of Data Science, Fudan University e1；Department of Applied Mathematics, The Hong Kong Polytechnic University；Department of Biostatistics, City University of Hong Kong；Eye Institute；Department of Ophthalmology, Eye \& ENT Hospital, Fudan University；Eunice Kennedy Shriver National Institute of Child Health；College of Business；Economics, Australian National University
OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images
Fudan University（复旦大学）；The Hong Kong Polytechnic University（香港理工大学）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）；Australian National University（澳大利亚国立大学）；Eunice Kennedy Shriver National Institute of Child Health and Human Development（尤尼斯·肯尼迪·施莱佛国家儿童健康与人类发育研究所）
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Electronic Science and Technology of China（电子科技大学）；West China Hospital, Sichuan University（四川大学华西医院）；Chengdu First People’s Hospital（成都市第一人民医院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；The Hong Kong University of Science and Technology（香港科技大学）
A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China（浙江工业大学信息工程学院）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China（中国科学院宁波材料技术与工程研究所）；Department of Eye and Vision Science, University of Liverpool, Liverpool, UK（利物浦大学眼科学与视觉科学系）；School of Biomedical Engineering, ShanghaiTech University, Shanghai, China（上海科技大学生物医学工程学院）
Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Electronic Science and Technology of China（电子科技大学）；Wuhan University（武汉大学）；Shanghai Jiao Tong University（上海交通大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Imperial College London（伦敦帝国学院）；The Hong Kong University of Science and Technology（香港科技大学）
Segmenting blood vessels in fundus imaging plays an important role in medical diagnosis. Many algorithms have been proposed. While deep Neural Networks have been attracting enormous attention from computer vision community recent years and several novel works have been done in terms of its application in retinal blood vessel segmentation, most of them are based on supervised learning which requires amount of labeled data, which is both scarce and expensive to obtain. We leverage the power of Deep Convolutional Neural Networks (DCNN) in feature learning, in this work, to achieve this ultimate g…
Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes us…
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
School of Optometry, The Hong Kong Polytechnic University（香港理工大学眼科视光学院）；Research Centre for SHARP Vision, The Hong Kong Polytechnic University（香港理工大学SHARP视觉研究中心）；Centre for Eye and Vision Research (CEVR)（眼科与视觉研究中心）；Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital（澳大利亚眼科研究中心，皇家维多利亚眼耳医院）；Department of Surgery (Ophthalmology), The University of Melbourne（墨尔本大学外科学系（眼科学））
Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation. Methods A …
Department of Computer Science University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；Department of Ophthalmology Central South University（中南大学湘雅医学院（注：原文仅显示Department of Ophthalmology，此处“Central South University”为大学，按要求提取到大学级别））
Diabetic retinopathy (DR) is a primary cause of blindness in working-age people worldwide. About 3 to 4 million people with diabetes become blind because of DR every year. Diagnosis of DR through color fundus images is a common approach to mitigate such problem. However, DR diagnosis is a difficult and time consuming task, which requires experienced clinicians to identify the presence and significance of many small features on high resolution images. Convolutional Neural Network (CNN) has proved to be a promising approach for automatic biomedical image analysis recently. In this work, we inves…
Sun Yat-sen University（中山大学）；Baidu Inc.（百度公司）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学技术研究委员会）；UNICEN（阿根廷中央国立大学）；Medical University of Vienna（维也纳医科大学）；VUNO Inc.（VUNO公司）；Tencent（腾讯）
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-dev…
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria（维也纳医科大学医学数据科学中心人工智能研究所）；Christian Doppler Lab for Artificial Intelligence in Retina, Medical University of Vienna, Austria（维也纳医科大学视网膜人工智能克里斯蒂安·多普勒实验室）
Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images
UNCPBA, CONICET, Yatiris Group, Pladema Institute（UNCPBA、CONICET、Yatiris集团、Pladema研究所）；Servicio de Oftalmología, Hospital de Alta Complejidad en Red ``El Cruce'' Dr. Néstor Carlos Kirchner（眼科服务，高级复杂性网络‘El Cruce’医院Dr. Néstor Carlos Kirchner）
Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Monash University（莫纳什大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）；The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼与视觉研究中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Airdoc-Monash Research Lab（Airdoc-莫纳什研究实验室）
Monash University（莫纳什大学）；Airdoc；NVIDIA AI Technology Center（英伟达人工智能技术中心）
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based retinal vessel segmentation algorithms have achieved remarkable performance. However, due to the domain shift problem, the performance of these algorithms often degrades when they are applied to new data that is different from the training data. Manually labeling new data for each test domain is often a time-consuming and laborious task. In this work, we explore unsupervised domain adaptation in retinal vessel segmen…
As machine learning has become increasingly applied to medical imaging data, noise in training labels has emerged as an important challenge. Variability in diagnosis of medical images is well established; in addition, variability in training and attention to task among medical labelers may exacerbate this issue. Methods for identifying and mitigating the impact of low quality labels have been studied, but are not well characterized in medical imaging tasks. For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer v…
